4 citations · 11 across the 4 of their papers we have counts for
4 papers
Optimized HDBSCAN clustering for reconstructing the merger history of the Milky Way: applications and limitations
Andrea Sante, Andreea S. Font, Dharmesh Mistry +2
Clustering algorithms can help reconstruct the assembly history of the Milky Way by identifying groups of stars sharing similar properties in a kinematical or chemical abundance sp…
Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies
Julieth Katherine Riveros, Paola Saavedra, Hector J. Hortua +2
Next-generation galaxy surveys promise unprecedented precision in testing gravity at cosmological scales. However, realising this potential requires accurately modelling the non-li…
Applying machine learning to Galactic Archaeology: how well can we recover the origin of stars in Milky Way-like galaxies?
Andrea Sante, Andreea S. Font, Sandra Ortega-Martorell +2
We present several machine learning (ML) models developed to efficiently separate stars formed in-situ in Milky Way-type galaxies from those that were formed externally and later a…
Machine Learning applications for Cataclysmic Variable discovery in the ZTF alert stream
D. Mistry, C. M. Copperwheat, I. Olier +1
Cataclysmic variables (CV) encompass a diverse array of accreting white dwarf binary systems. Each class of CV represents a snapshot along an evolutionary journey, one with the pot…